U.S. patent application number 13/644838 was filed with the patent office on 2014-04-10 for network capacity planning.
This patent application is currently assigned to VERIZON PATENT AND LICENSING INC.. The applicant listed for this patent is VERIZON PATENT AND LICENSING INC.. Invention is credited to David CHIANG, Emerando M. DELOS REYES, Chris S. NEISINGER, Brian OLSON, Scott A. TOWNLEY.
Application Number | 20140101297 13/644838 |
Document ID | / |
Family ID | 50433645 |
Filed Date | 2014-04-10 |
United States Patent
Application |
20140101297 |
Kind Code |
A1 |
NEISINGER; Chris S. ; et
al. |
April 10, 2014 |
NETWORK CAPACITY PLANNING
Abstract
A network device is configured to receive information relating
to factors associated with quality of experience issues. The
network device is configured to analyze the information. The
network device is configured to predict that a quality of
experience factor associated with a particular type of
communication will exceed a threshold level a future time. The
network device is configured to send a message to the device, the
device generating a rule or policy; and the rule or policy
instructing one or more other network devices to increase a
capacity of the network to prevent the quality of experience factor
from exceeding the threshold value at the future time.
Inventors: |
NEISINGER; Chris S.;
(Danville, CA) ; OLSON; Brian; (Clayton, CA)
; DELOS REYES; Emerando M.; (Pleasant Hill, CA) ;
CHIANG; David; (Fremont, CA) ; TOWNLEY; Scott A.;
(Gilbert, AZ) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
VERIZON PATENT AND LICENSING INC. |
Basking Ridge |
NJ |
US |
|
|
Assignee: |
VERIZON PATENT AND LICENSING
INC.
Basking Ridge
NJ
|
Family ID: |
50433645 |
Appl. No.: |
13/644838 |
Filed: |
October 4, 2012 |
Current U.S.
Class: |
709/223 |
Current CPC
Class: |
H04L 43/16 20130101;
H04L 41/0893 20130101; H04L 41/5067 20130101; H04L 41/0896
20130101; H04L 41/147 20130101; H04L 43/087 20130101; H04L 43/0829
20130101; H04L 65/4084 20130101; H04L 43/026 20130101; H04L 43/0852
20130101; H04L 65/80 20130101 |
Class at
Publication: |
709/223 |
International
Class: |
G06F 15/173 20060101
G06F015/173 |
Claims
1. A system comprising: a network device to: receive information
relating to factors associated with quality of experience issues;
analyze the information; predict, based on a result of analyzing
the information, that a quality of experience factor, associated
with a particular type of communication within a particular
coverage area of a network, will exceed a threshold level at a
future time; and send a message to a device, based on predicting
that the quality of experience factor within the particular
coverage area of the network will exceed the threshold level, at
the future time, the device generating or changing a rule or policy
associated with the particular coverage area of the network based
on the message, the rule or policy instructing one or more other
network devices, in the network, to increase a capacity of the
particular coverage area of the network to prevent the quality of
experience factor from exceeding the threshold value at the future
time.
2. The system of claim 1, where, when receiving the information,
the network device is to: receive information identifying a
particular session between the network and a user device.
3. The system of claim 1, where, when receiving the information,
the network device is to: receive information identifying a
streaming video communication between the network and a user
device.
4. The system of claim 1, where, when predicting that the quality
of experience factor will exceed the threshold value, the network
device is to: predict the affect of exceeding the threshold level
will have on the quality of experience for a user device located in
the particular coverage area of the network.
5. The system of claim 4, where, when analyzing the information,
the network device is to: analyze the information by performing
linear regression analysis.
6. The system of claim 4, where, when predicting that the quality
of experience factor will exceed the threshold level, the network
device is to: compare the analysis of the information with
historical information for other coverage areas of the network; and
predict, based on the comparison, that the quality of experience
will exceed the threshold level at the future time.
7. The system of claim 1, where, when receiving the information,
the network device is to: receive information regarding timestamp
information associated with when the information is obtained from
the network; and receive identifier information for a cell region
within the particular coverage area of the network.
8. A method comprising: receiving, by a network device, information
relating to factors associated with quality of experience issues;
analyzing, by the network device, the information; predicting, by
the network device, that a quality of experience factor associated
with a particular type of communication will exceed a threshold
level at a future time; and sending, by the network device, a
message to a device based on predicting that the quality of
experience factor will exceed the threshold level at the future
time, the device generating a notification to the network to
prevent issues relating to quality of experience from occurring in
the future.
9. The method of claim 8, where receiving the information includes:
receiving information regarding an interactive video communication
between a user device and the network; and receiving information
regarding average session setup, delay time, average session setup,
and failure rate.
10. The method of claim 8, where analyzing of the information
includes: analyzing information associated with quality of
experience factors; and analyzing information associated with
non-quality of experience factors.
11. The method of claim 10, where the information associated with
quality of experience factors includes factors associated with
delay, loss, and failure issues.
12. The method of claim 8, where analyzing the information
includes: analyzing the information using linear regression.
13. The method of claim 12, where receiving information includes:
receiving information associated with a conversational voice
communication, and where the method further includes: receiving
additional information associated with communications regarding
Internet browsing; analyzing the additional information; and
predicting that another quality of experience factor associated
with Internet browsing will reach the threshold level at a
different future time.
14. The method of claim 8, where sending the message to the device
includes: sending a notification to increase a number of cells
within a particular coverage area of a network.
15. The method of claim 8, where sending the message to the device
includes: sending a notification to increase a number of channels
within a particular coverage area of a network.
16. A computer-readable medium comprising: a plurality of
instructions, that when executed by one or more processors of one
or more network devices, cause the one or more processors to:
receive information relating to factors associated with quality of
experience issues; analyze the information; predict, based on a
result of analyzing the information, that a quality of experience
factor, associated with a particular type of communication, will
exceed a threshold level at a future time; and send a message to a
device, based on predicting when the quality of experience factor
within a particular coverage area of a network will exceed the
threshold value at the future time, the device generating or
changing a rule or policy associated with the particular coverage
area of the network based on the message, the rule or policy
instructing one or more other network devices, in the network, to
increase a capacity of the particular coverage area of the network
to prevent the quality of experience factor from exceeding the
threshold value at the future time.
17. The computer-readable medium of claim 16, where one or more
instructions, of the plurality of instructions, to receive the
information include one or more instructions to: receive
information associated with an interactive video, the information
including average session setup delay time, average session setup
failure rate, packet loss rate, and jitter rate.
18. The computer-readable medium of claim 16, where one or more
instructions, of the plurality of instructions, to send the message
to the device include one or more instructions to: send a message
to the device to increase a number of channels within the
particular coverage area of the network.
19. The computer-readable medium of claim 16, where one or more
instructions, of the plurality of instructions, to send the message
to the device include one or more instructions to: send a message
to the device to increase a number of cells within the particular
coverage area of the network.
20. The computer-readable medium of claim 16, where the plurality
of instructions further cause the one or more processors to:
receive additional information associated with machine to machine
communications within the particular coverage area of the network;
analyze the additional information; and predict when another
quality of experience factor, associated with the machine to
machine communications within the particular coverage area of the
network, will exceed a different threshold level at a different
future time.
Description
BACKGROUND
[0001] A network may provide service to a user device connected to
the network. During the operation of the network, the network, or a
part of the network, may reach its maximum capacity level. As the
network, or part of the network, reaches, or exceeds, the maximum
capacity level, the user, of the user device, may experience
quality of experience (QoE) issues, such as delay, failure of
service, and/or other issues.
BRIEF DESCRIPTION OF DRAWINGS
[0002] FIG. 1 is a diagram of an overview of an implementation
described herein;
[0003] FIG. 2 is a diagram of an example environment in which
systems and/or methods described herein may be implemented;
[0004] FIG. 3 is a diagram of example components of one or more
devices of FIGS. 1 and 2;
[0005] FIG. 4 is a flow chart of an example process for analyzing
the capacity of a network; and
[0006] FIGS. 5A-5B are diagrams of example processes for analyzing
the capacity of a network.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0007] The following detailed description refers to the
accompanying drawings. The same reference numbers in different
drawings may identify the same or similar elements.
[0008] Systems and/or methods described herein may analyze
information regarding the threshold level for a QoE factor for a
particular type of communication; determine whether the QoE factor
for the particular type of communication within the network has
reached the threshold level and/or predict when the QoE factor for
the particular type of communication in the network may reach the
threshold level; and implement solutions to resolve any potential
QoE issues by increasing the capacity level of the network.
[0009] FIG. 1 is a diagram of an overview of an implementation
described herein. As shown in FIG. 1, a user ("Tim") is using his
smart phone (Tim's smart phone) to watch a movie that he is
receiving from Sci-Fi movie provider via a network. As Tim is
watching the movie, the network analysis device may be receiving
information about the delay rates for streaming video content
within a particular coverage area of the network. The network
analysis device may receive communication information between
different user devices associated with a particular coverage area
of the network, including Tim's smart phone (which is being used to
receive streaming video content) that is also located in that
particular coverage area of the network. The network analysis
device may analyze whether the delay rate for streaming video
content in the particular coverage area of the network has exceeded
a threshold level. The threshold level indicates the maximum level
of the delay rate that is allowable before the user (Tim) begins to
have QoE issues. The network analysis device may determine that the
delay rate, for streaming video content, in the particular coverage
area of the network has not reached the threshold level. The
network analysis device may predict that the delay rate, for
streaming video content, in the particular coverage area of the
network may reach the threshold level within the next 24 hours. The
network analysis device may send a message to a computer. The
computer is associated with the network analysis device and the
network service provider. A user, of the computer, may view the
message (sent from the network analysis device) and may have
different options on how to provide additional capacity, such as
adding additional channels, to the particular coverage area of the
network to prevent the delay rate from exceeding the threshold
level as predicted within the next 24 hours.
[0010] As a result, a network may operate more efficiently based on
an analysis system that is able to detect when users may experience
QoE issues before a QoE factor exceeds a threshold level. With the
ability to predict future QoE issues, the network may provide a
greater level of QoE to the users of the network.
[0011] FIG. 2 is a diagram of an example environment 200 in which
systems and/or methods described herein may be implemented. As
shown in FIG. 2, environment 200 may include a user device 205, a
device 210, a data collection device 215, an analysis center 220, a
data memory device 225, a content provider 230, an analysis memory
device 235, network 240, and a network 250.
[0012] User device 205 may include any computation or communication
device, such as a wireless mobile communication device that is
capable of communicating with a network (e.g., network 240 and/or
network 250). For example, user device 205 may include a
radiotelephone, a personal communications system (PCS) terminal
(e.g., that may combine a cellular radiotelephone with data
processing and data communications capabilities), a personal
digital assistant (PDA) (e.g., that can include a radiotelephone, a
pager, Internet/intranet access, etc.), a smart phone, a computer,
a laptop, a tablet computer, a camera, a personal gaming system, a
television, or another mobile, computation, or communication
device.
[0013] User device 205 may include a variety of applications, such
as, for example, an e-mail application, a telephone application, a
camera application, a video application, a multi-media application,
a music player application, a visual voicemail application, a
contacts application, a data organizer application, a calendar
application, an instant messaging application, a texting
application, a web browsing application, a location-based
application (e.g., a GPS-based application), a blogging
application, and/or other types of applications (e.g., a word
processing application, a spreadsheet application, etc.).
[0014] Device 210 may include any computation or communication
device that is capable of communicating with a network (e.g.,
network 240 and/or network 250). Device 210 may include a computer,
a laptop, a workstation, or another device capable of receiving
information and displaying the information to a user of device
210.
[0015] Data collection device 215 may include one or more network
devices, or other types of computation or communication devices,
that gather, process, search, and/or provide information in a
manner described herein. Data collection device 215 may request
and/or receive information associated with the operation of network
240. Data collection device 215 may be a part of analysis center
220 or data collection device 215 may be a separate device than
analysis center 220.
[0016] Analysis Center 220 may include one or more network devices,
or other types of computation or communication devices, that
gather, process, search, and/or provide information in a manner
described herein. Analysis center 220 may analyze various issues
relating to QoE factors that are associated with the capacity level
of a network.
[0017] Data memory device 225 may include one or more memory, or
network, devices that gather, process, store and/or provide
information described herein. Data memory device 225 may store
information collected by data collection device 215. Data memory
device 225 may be a part of data collection device 215 or data
memory device 225 may be a separate device than data collection
device 215.
[0018] Content provider 230 may include one or more network
devices, or other types of computation or communication devices
that gather, process, and/or provide information in a manner
described herein. For example content provider 230 may send, via
network 240, content to user device 205. The content is intended to
be broadly interpreted to include any computer readable data that
may be transferred over a network. Content may include objects,
data, images, audio, video, text, files, and/or links to files
accessible via one or more networks. Content may include a media
stream, which may refer to a stream of content that includes video
content (e.g., a video stream), audio content (e.g., an audio
stream), and/or textual content (e.g., a textual stream).
[0019] Analysis memory device 235 may include one or more memory,
or network, devices that gather, process, store and/or provide
information described herein. Analysis memory device 235 may
receive analysis information from analysis center 220 and may store
the analysis information and/or send the analysis information to
device 210. Analysis memory device 235 may be part of analysis
center 220 or analysis memory device 235 may be a separate
device.
[0020] Network 240 and/or network 250 may include a cellular
network, a public land mobile network (PLMN), a second generation
(2G) network, a third generation (3G) network, a fourth generation
(4G) network, a fifth generation (5G) network and/or another
network. Additionally, or alternatively, network 240 and/or network
250 may include a local area network (LAN), wide area network
(WAN), a metropolitan network (MAN), a telephone network (e.g., the
Public Switched Telephone Network (PSTN)), an ad hoc network, an
intranet, the Internet, a satellite network, a GPS network, a fiber
optic-based network, and/or combination of these or other types of
networks. Additionally, or alternatively, network 240 and/or
network 250 may support secure communications via a private network
(e.g., a virtual private network (VPN) or a private IP VPN (PIP
VPN), and/or secure communications via a public network.
[0021] Additionally, or alternatively, network 240 and/or network
250 may include a radio access network (RAN), such as a long term
evolution (LTE) network, that may include a variety of components
to facilitate mobile communications, such as antennas, base
stations, mobile switching centers, and interfaces with Public
Switched Telephone Networks (PSTNs) and/or packet data servicing
nodes (PDSNs).
[0022] Network 240 and network 250 may be the separate networks, or
network 240 and network 250 may be part of the same network.
[0023] In some implementations, communications between user device
205 and other devices (associated with network 240 and/or network
250) may be via data packets. The data packets may be defined as
Internet Protocol (IP) data packets (associated with IP version 4
(IPv4), IP version 6 (IPv6), or any other IP version), session
initiation protocol (SIP) data packets, or some other form or
arrangement of data.
[0024] The quantity of devices and/or networks, illustrated in FIG.
2 is provided for explanatory purposes only. In practice, there may
be additional devices and/or networks; fewer devices and/or
networks; different devices and/or networks; and differently
arranged devices and/or networks than illustrated in FIG. 2. Also,
in some implementations, one or more of the devices of environment
200 may perform one or more functions described as being performed
by another one or more of the devices of environment 200. Devices
of environment 200 may interconnect via wired connections, wireless
connections, or a combination of wired and wireless
connections.
[0025] FIG. 3 is a diagram of example components of a device 300.
Device 300 may correspond to user device 205, device 210, data
collection device 215, analysis center 220, data memory device 225,
content provider 230, and/or analysis memory device 235.
Additionally, or alternatively, each of user device 205, device
210, data collection device 215, data memory device 225, analysis
center 220, content provider 230, and/or analysis memory device 235
may include one or more devices 300 and/or one or more components
of device 300.
[0026] As shown in FIG. 3, device 300 may include a bus 310, a
processor 320, a memory 330, an input component 340, an output
component 350, and a communication interface 360. In other
implementations, device 300 may contain fewer components,
additional components, different components, or differently
arranged components than depicted in FIG. 3. Additionally, or
alternatively, one or more components of device 300 may perform one
or more tasks described as being performed by one or more other
components of device 300.
[0027] Bus 310 may include a path that permits communication among
the components of device 300. Processor 320 may include one or more
processors, microprocessors, or processing logic (e.g., a field
programmable gate array (FPGA), or an application specific
integrated circuit (ASIC)) that interprets and executes
instructions. Memory 330 may include any type of dynamic storage
device that stores information and instructions, for execution by
processor 320, and/or any type of non-volatile storage device that
stores information for use by processor 320.
[0028] Input component 340 may include a mechanism that permits a
user to input information to device 300, such as a keyboard, a
keypad, a button, a switch, etc. Output component 350 may include a
mechanism that outputs information to the user, such as a display,
a speaker, one or more light emitting diodes (LEDs), etc.
[0029] Communication interface 360 may include any transceiver-like
mechanism that enables device 300 to communicate with other devices
and/or systems. For example, communication interface 360 may
include an Ethernet interface, an optical interface, a coaxial
interface, a wireless interface, or the like.
[0030] In another implementation, communication interface 360 may
include, for example, a transmitter that may convert baseband
signals from processor 320 to radio frequency (RF) signals and/or a
receiver that may convert RF signals to baseband signals.
Alternatively, communication interface 360 may include a
transceiver to perform functions of both a transmitter and a
receiver of wireless communications (e.g., radio frequency,
infrared, visual optics, etc.), wired communications (e.g.,
conductive wire, twisted pair cable, coaxial cable, transmission
line, fiber optic cable, waveguide, etc.), or a combination of
wireless and wired communications.
[0031] Communication interface 360 may connect to an antenna
assembly (not shown in FIG. 3) for transmission and/or reception of
the RF signals. The antenna assembly may include one or more
antennas to transmit and/or receive RF signals over the air. The
antenna assembly may, for example, receive RF signals from
communication interface 360 and transmit the RF signals over the
air, and receive RF signals over the air and provide the RF signals
to communication interface 360. In one implementation, for example,
communication interface 360 may communicate with other networks
and/or devices connected to network 240 and/or network 250.
[0032] As will be described in detail below, device 300 may perform
certain operations. Device 300 may perform these operations in
response to processor 320 executing software instructions (e.g.,
computer program(s)) contained in a computer-readable medium, such
as memory 330, a secondary storage device (e.g., hard disk, CD-ROM,
etc.), or other forms of RAM or ROM. A computer-readable medium may
be defined as a non-transitory memory device. A memory device may
include space within a single physical storage device or spread
across multiple physical storage devices. The software instructions
may be read into memory 330 from another computer-readable medium
or from another device. The software instructions contained in
memory 330 may cause processor 320 to perform processes described
herein. Alternatively, hardwired circuitry may be used in place of
or in combination with software instructions to implement processes
described herein. Thus, implementations described herein are not
limited to any specific combination of hardware circuitry and
software.
[0033] FIG. 4 is a flow chart of an example process 400 for
analyzing the capacity of a network. In one implementation, process
400 may be performed by analysis center 220. In another example
implementation, one or more blocks of process 400 may be performed
by one or more other devices, such as data collection device 215
and/or device 210.
[0034] Process 400 may include receiving information about the
network (block 410). Analysis center 220 may receive information
about network 240 from data memory device 225. Data memory device
225 may receive the information from data collection device 215.
Data collection device 215 may include or interact with different
types of data collection applications to receive information about
different network devices and/or interfaces associated with a
session between user device 205 and network 240. These different
types of data collection applications may send requests for
information to different network devices within network 240 and/or
different interfaces between different network devices for
information about network operations. The different types of data
collection applications may obtain the information in real time.
The different types of data collection applications may perform
deep packet inspection across various user plane and control plane
network interfaces.
[0035] The data collection applications may include one or more of
the following: simple network management protocol (hereinafter
referred to as "SNMP"), Syslog, central processing unit utilization
(hereinafter referred to as "CPU utilization"), Netflow, session
initiation protocol flow information exchange (hereinafter referred
to as "SIPFix"), internet protocol flow information exchange
(hereinafter referred to as "IPFix"), subscriber packet data,
signaling links, and/or any other type of data collection
application.
[0036] SNMP may be a protocol used to monitor the activities of
network devices. Each network device may have a SNMP interface that
may permit analysis center 220 to obtain information regarding the
activity of the network device. Syslog may store information
associated with computer data logging that may provide information
to analysis center 220 regarding the activity of the network. CPU
utilization may include information about the amount of time that a
network device is active. SIPFix may provide analysis center 220
with information associated with flow rates of data packets and/or
the amount of information, associated with SIP data packets, being
sent to/from network devices.
[0037] IPFix and/or Netflow may provide analysis center 220 with
information associated with flow rates of data packets and/or the
amount of information, associated with IP data packets, being sent
to/from network devices. For example, in an LTE network, IPFix may
provide information associated with flow rates of data packets
being sent between different network devices. For example, in an
LTE network, IPFix may obtain information being sent (e.g., via an
S1-U interface) between a serving gateway (hereinafter referred to
as "SGW") and a base station. Additionally, or alternatively, in an
LTE network, IPFix may obtain information being sent (e.g., via an
S11 interface) between mobility management entity device
(hereinafter referred to as "MME") and a packet data network (PDN)
gateway (hereinafter referred to as PGW).
[0038] Subscriber packet data may include information about data
packets associated with applications and/or services that are being
used by a user of user device 205. Signaling links may provide
analysis center 220 with information associated with the set-up,
management, and/or tear down of communications between user device
205 and network 240.
[0039] The different data collection applications within data
collection device 215 may send the information to data memory
device 225, for the session between user device 205 and network 240
according to different QoE factors. For example, the QoE factor may
be associated with delay, loss, failure, throughput, and/or other
types of QoE factors. Data memory device 225 may store the session
information based on an identifier for the session. Additionally,
or alternatively, data collection device 215 may receive different
types of information associated with QoE factors for different
types of communications and send the information to data memory
device 225.
[0040] For example, for a conversational voice communication
(between user device 205 and network 240), data collection device
215 may collect information associated with average session setup
delay time, average session setup failure rate, average mean
opinion score, packet loss rate, jitter, and/or other information.
A conversational voice communication may include a voice over LTE
communication or a third party over-the-top (OTT) voice over IP
(VoIP) communication.
[0041] In another example, for an interactive video communication
(between user device 205 and network 240), data collection device
215 may collect information associated with average session setup
delay time, average session setup failure rate, video opinion
score, packet loss, jitter, and/or other information. An
interactive video communication may include a video call made using
network devices in network 240 or a third party OTT video call.
[0042] In another example, for a streaming video communication
(between user device 205 and network 240), data collection device
215 may collect information associated with average throughput
rates, average session setup delay time, average setup failure
rate, average buffering/stalling event rate, average video opinion
score, packet loss, and other information. A streaming video
communication may include adaptive streaming or a progressive
download.
[0043] In other examples, such as for Internet browsing
communications, machine to machine communication, or other types of
IP traffic (between user device 205 and network 240), data
collection device 215 may collect information including average
throughput rates, average session setup delay time, average session
setup failure rate, and/or other information. Data collection
device 215 may send the information to data memory device 225.
[0044] Additionally, or alternatively, data collection device 215
may send, to data memory device 225, information that includes a
timestamp and/or an identifier for the particular coverage area of
the network. For example, the identifier may identify an individual
cell region within the network, a group of cell regions, or a
different category regarding a coverage area of the network. The
timestamp information may indicate when the information was
collected by data collection device 215.
[0045] Process 400 may include analyzing the information about the
network (block 420). Analysis center 220 may analyze the
information (described with regard to block 410) received from data
memory device 225.
[0046] Analysis center 220 may derive a daily busy hour for a
particular coverage area of the network. The busy hour may be
derived by determining a maximum traffic intensity based on several
factors, such as average communication time, and the number of
attempts to make a communication per unit of time. Analysis center
220 may derive a daily QoE factor (e.g., delay) for a particular
type of application (e.g., streaming video) in a particular
coverage area (e.g., cell area) within the network during the busy
hour of the particular coverage area. Additionally, analysis center
220 may derive a daily non-QoE factor (e.g., flow rates or total
power usage within the particular coverage area) for a particular
type of application in a particular coverage area within the
network during the busy hour of the particular coverage area.
[0047] Analysis center 220 may compare the QoE factor, during the
busy hour of the particular coverage area with the threshold level
associated with the particular QoE factor within the particular
coverage area of the network. The threshold level may indicate the
maximum value of the QoE factor that may occur during the busy hour
of the particular coverage area of the network without any QoE
issues occurring for the user (e.g., no sound distortion, pixels
appearing within a screen, etc.). In one example implementation,
analysis center 220 may compute the threshold level for an interval
of time (e.g., busy hour for every hour, every day, every three
days, every week, every month, etc.). In another example
implementation, device 210 may provide the threshold level to
analysis center 220. A user, of device 210, may determine the
threshold level for the QoE factor and send the threshold level
information, via device 210 to analysis center 220. Different types
of communications may have different threshold levels for different
QoE factors within the particular coverage area of the network. For
example, a threshold level for conversational voice communications,
in a particular coverage area of the network, may be different
(e.g., greater or less) than a threshold level for streaming video.
Additionally, analysis center 220 may compare the non-QoE factor,
during the busy hour of the particular coverage area, with the
threshold level associated with the non-QoE factor within the
particular coverage area of the network.
[0048] In some implementations, analysis center 220 may
automatically determine, based on the analysis, that additional
cells and/or additional channels may be added to the particular
coverage area. Analysis center 220 may send a notification to
network 240 to implement the requested changes. Network 240 may
receive the notification and the network elements may implement the
requested changes, such as adding additional cells to the coverage
area.
[0049] If the capacity threshold is exceeded (block 425--YES), then
process 400 may include sending the analysis regarding the network
(block 430). For example, analysis center 220 may determine that
the QoE factor associated with the daily busy hour exceeds a
particular threshold level. Analysis center 220 may send the
analysis to analysis memory device 235. Analysis memory device 235
may store the analysis and may send the analysis to device 210. A
user (using device 210), may view the analysis and determine (using
device 210) how to provide additional capacity to the particular
coverage area of the network. The user may determine that
additional cells and/or additional channels may be added to the
particular coverage area. The user may send, using device 210, a
notification to network 240 to implement the requested changes.
Network 240 may receive the notification and the network elements
within network 240 may implement the requested changes, based on
the notification, such as adding additional cells.
[0050] If the capacity threshold is not exceeded (block 425--NO),
then process 400 may include performing additional analysis (block
435). For example, analysis center 220 may perform additional
analysis to predict when the particular coverage area may reach the
maximum capacity level for the particular QoE factor.
[0051] Analysis center 220 may use different types of analysis to
predict when the capacity for a particular coverage area may exceed
the threshold level of the particular coverage area. For example,
analysis center 220 may use linear regression analysis to determine
the predicted time and/or date that the capacity of the particular
coverage area may exceed the threshold level for the particular
coverage area.
[0052] Additionally, or alternatively, analysis center 220 may
perform other types of analysis to further refine the prediction.
Analysis center 220 may perform pattern matching and/or other types
of historical analysis to compare the prediction with other
coverage areas of the network. For example, analysis center 220 may
analyze historical values for a QoE factor within one or more other
coverage areas (such as a cell region). Additionally, or
alternatively, analysis center 220 may determine whether there are
any patterns or trends associated with the QoE factor in the other
coverage areas. For example, analysis center 220 may determine
whether, during a busy hour on a particular day (e.g., Monday), the
setup delay times for streaming video communications begin to
exceed the threshold value for setup delay times for streaming
video communications. Additionally, or alternatively, analysis
center 220 may perform heuristic or other types of predictive
and/or statistical analysis to further refine the prediction.
[0053] Additionally, during the analysis, analysis center 220 may
include information regarding non-QoE factors and use those values
within the different types of analysis, discussed above. For
example, analysis center 220 may analyze power consumption
(associated with multiple user devices 205) within the particular
coverage area to determine when the amount of power consumption
will exceed the threshold level for power consumption in the
particular coverage area. Analysis center 220 may, for example,
analyze flow rates and determine that a trend of increasing flow
rates may indicate when the particular type of communication (e.g.,
Internet browsing) may exceed the threshold level for flow rates in
the particular coverage area.
[0054] If there is not a match with the previous history (block
440--NO), then process 400 may include sending the analysis (block
445). For example, analysis center 220 may determine that that
there is no similarity (e.g., no similar trends or values) between
the QoE factor for the other coverage areas and the particular
coverage area that is being analyzed. Analysis center 220 may send
the analysis to analysis memory device 235. Analysis memory device
235 may store the analysis and may send the analysis to device 210.
A user, using device 210, may view the analysis and determine,
using device 210, how to provide additional capacity to the
particular coverage area of the network. This may include
generating or changing rules and/or policies to increase the
capacity of the particular coverage area of the network. For
example, the user may determine that additional cells may be added
to the particular coverage area that will increase the capacity of
the particular coverage area and prevent the QoE factor from
exceeding the threshold level. The user may send, using device 210,
a notification to network 240 to implement the requested
changes.
[0055] In some implementations, analysis center 220 may
automatically make the determination, based on the analysis,
whether to provide additional cells or channels for the particular
coverage area.
[0056] If there is a match with the previous history (block
440--YES), then process 400 may include performing additional
analysis (block 450). For example, analysis center 220 may
determine that that there is a match between the QoE factor for the
other coverage areas and the particular coverage area that is being
analyzed. Analysis center 220 may further analyze the information
by using pattern matching and/or other historical analysis methods,
as described in block 435.
[0057] Process 400 may include sending the analysis (block 460).
Analysis center 220 may send the analysis to analysis memory device
235. Analysis memory device 235 may store the analysis and may send
the analysis to device 210, described with regard to block 445.
[0058] While a series of blocks has been described with regard to
FIG. 4, the order of the blocks may be modified in other
implementations. Further, non-dependent blocks may be performed in
parallel.
[0059] FIGS. 5A-5B are diagrams of example processes for analyzing
a network. FIG. 5A shows data collection device 215, analysis
center 220, network 240, network 250, user device 505, and Movies
Plus server 510. An example of user device 505 may correspond to
user device 205, described with regard to FIG. 2. An example of
Movies Plus server 510 may correspond to content provider 230. For
the purpose of this example, assume that data collection device 215
performs the functions of data memory device 225, and analysis
center 220 performs the functions of analysis memory device
235.
[0060] As shown in FIG. 5A, a user ("Tom") is using his smart phone
(user device 505) to watch movies from Movies Plus. To watch the
movie, user device 505 sends a message, via network 240, to Movies
Plus server 510, via network 240. As information is being sent
to/from network 240, data collection device 215 may be collecting
data about the information and sending the data to analysis center
220.
[0061] FIG. 5B shows data collection device 215, analysis center
220, network 240, and device 530. An example of device 530 may
correspond to device 210, described with regard to FIG. 2. Data
collection device 215 may receive information relating to average
throughput rates, average session setup delay time, and average
session setup failure rate. Data collection device 215 may also
receive other network information, such as data flow information
and also an identifier identifying the cell in which user device
505 is located.
[0062] Data collection device 215 may send the collected
information to analysis center 220. Analysis center 220 may analyze
the information received from data collection device 215. Analysis
center 220 may determine that, at the present time, the average
session setup delay time has not reached the threshold level during
a busy hour of the day. Through additional analysis, analysis
center 220 may predict that, in 36 hours, the average session setup
delay time will reach the threshold level.
[0063] As shown in FIG. 5B, analysis center 220 may send the
prediction to device 530. Device 530 is being used by an employee
of the provider of network 240. The employee is able to view a
message (sent from analysis center 220) that notifies the employee
that the average setup delay time (for streaming videos in that
particular coverage area) will exceed the threshold value in 36
hours. The employee decides to select an option to increase the
number of channels within the cell region. By selecting the option,
device 530 may send a message to network 240 (via network 250) to
implement the increased number of channels for the cell region.
[0064] As a result, a network may operate more efficiently based on
an analysis system that is able to detect when users may experience
QoE issues before a QoE factor exceeds the threshold level. With
the ability to predict future QoE issues, the network may provide a
greater level of QoE to the users of the network.
[0065] The foregoing description of implementations provides
illustration and description, but is not intended to be exhaustive
or to limit the implementations to the precise form disclosed.
Modifications and variations are possible in light of the above
disclosure or may be acquired from practice of the
implementations.
[0066] It will be apparent that example aspects, as described
above, may be implemented in many different forms of software,
firmware, and hardware in the implementations illustrated in the
figures. The actual software code or specialized control hardware
used to implement these aspects should not be construed as
limiting. Thus, the operation and behavior of the aspects were
described without reference to the specific software code--it being
understood that software and control hardware could be designed to
implement the aspects based on the description herein.
[0067] Even though particular combinations of features are recited
in the claims and/or disclosed in the specification, these
combinations are not intended to limit the disclosure of the
possible implementations. In fact, many of these features may be
combined in ways not specifically recited in the claims and/or
disclosed in the specification. Although each dependent claim
listed below may directly depend on only one other claim, the
disclosure of the possible implementations includes each dependent
claim in combination with every other claim in the claim set.
[0068] No element, act, or instruction used in the present
application should be construed as critical or essential unless
explicitly described as such. Also, as used herein, the article "a"
is intended to include one or more items and may be used
interchangeably with "one or more." Where only one item is
intended, the term "one" or similar language is used. Further, the
phrase "based on" is intended to mean "based, at least in part, on"
unless explicitly stated otherwise.
[0069] In the preceding specification, various preferred
embodiments have been described with reference to the accompanying
drawings. It will, however, be evident that various modifications
and changes may be made thereto, and additional embodiments may be
implemented, without departing from the broader scope of the
invention as set forth in the claims that follow. The specification
and drawings are accordingly to be regarded in an illustrative
rather than restrictive sense.
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